Personalized functional brain network topography is associated with individual differences in youth cognition

Individual differences in cognition during childhood are associated with important social, physical, and mental health outcomes in adolescence and adulthood. Given that cortical surface arealization during development reflects the brain’s functional prioritization, quantifying variation in the topography of functional brain networks across the developing cortex may provide insight regarding individual differences in cognition. We test this idea by defining personalized functional networks (PFNs) that account for interindividual heterogeneity in functional brain network topography in 9–10 year olds from the Adolescent Brain Cognitive Development℠ Study. Across matched discovery (n = 3525) and replication (n = 3447) samples, the total cortical representation of fronto-parietal PFNs positively correlates with general cognition. Cross-validated ridge regressions trained on PFN topography predict cognition in unseen data across domains, with prediction accuracy increasing along the cortex’s sensorimotor-association organizational axis. These results establish that functional network topography heterogeneity is associated with individual differences in cognition before the critical transition into adolescence.

Supplementary Figure 2. Hexplots of associations between actual and predicted cognitive performance from ridge regression models.Association between actual and predicted cognitive performance using two-fold cross-validation (2F-CV) with nested cross-validation for parameter tuning across both the discovery (top row; n = 3,525) and replication (bottom row; n = 3,447) samples, for predictions of General Cognition (first column), Executive Function (second column), and Learning/Memory (third column).Heatmap represents the density of points plotted in a given region.

Supplementary Table 1. Predictive models incorporating socio-economic status (SES).
Prediction accuracy, measured as the Pearson correlation r between actual and predicted cognitive performance with raw p-values, is shown for ridge regression models trained to predict cognitive performance across three domains (General Cognition, Executive Function, and Learning/Memory) across both discovery and replication samples.Results from three sets of predictive models are shown: "SES" refers to models trained only on socio-economic status as measured by the areal deprivation index; "PFN Topography" refers to models trained on the multivariate pattern of personalized functional brain network (PFN) topography for each individual (as presented in the main text and in Figures 2 and 3); and "SES + PFN Topography" refers to models trained on both socio-economic status and PFN topography.Although SES is a significant predictor of cognitive functioning, models trained on PFN topography yield much stronger predictions of cognitive performance than SES alone, and the addition of SES information to models trained on PFN topography does not substantially increase prediction accuracy.This observation suggests that the spatial topography of individually-defined functional brain networks accounts for additional inter-individual variance in cognitive performance beyond what is accounted for by SES alone.

Prediction
PCA in Independent Discovery and Replication Samples.To avoid contamination across discovery(n = 3,525)  and replication (n = 3,447) samples in the cognitive outcome score that could lead to overfitting, we re-computed the cognitive domains of general cognition, executive function, and learning/memory using principal components analysis (PCA) conducted independently in the discovery and replication samples.Scatterplots depict the association between actual and predicted cognitive performance from ridge regression models trained to predict individual differences in general cognition (a), executive function (b), and learning/memory (c), using 2F-CV across both the discovery (black scatterplot) and replication (gray scatterplot) samples.Inset histograms represent the distributions of prediction accuracies from a permutation test.Brain maps depict the prediction accuracy results for ridge regression models trained to predict general cognition (d), executive function (e), or learning/memory (f) from the spatial topography of each PFN independently, with the highest prediction accuracies found in association cortex.

Figure 6 .
Prediction accuracy and S-A axis rank by PFN size.Prediction accuracies by network (the average Pearson correlation r between actual and predicted cognitive performance across discovery (n = 3,525) and replication (n = 3,447) samples) from ridge regression models trained on the vertex-wise pattern of topography for each PFN to predict (a) General Cognition, (b) Executive Function, or (c) Learning/Memory are plotted on the y-axes.PFN sizes, defined as the number of vertices belonging to each PFN in the hard parcellation, are plotted on the x-axes.The correlations between PFN size and prediction accuracy are statistically significant for all three cognitive domains (General Cognition: r(17) = 0.80, p < 0.001; Executive Function: r(17) = 0.52, p = 0.032; Learning/Memory: r(17) = 0.57, p = 0.017).(d) Association between sensorimotor-association (S-A) axis rank and PFN size (r(17) = 0.44, p = 0.077).

Table 2 . Sensitivity analyses controlling for psychotropic medication use.
Linear mixed effects models associating general cognition with the total cortical representation of fronto-parietal PFNs remain significant across both the discovery and replication samples when controlling for psychotropic medication use (assessed by the Medication Inventory from the PhenX instrument and coded as in Shoval et al., 2021; though we note that it is not clear from these measures whether psychotropic medications were taken on the same day as the neuroimaging assessments).P-values are corrected for multiple comparisons using Bonferroni correction.

.66 x 10 -6 Supplementary Table 3. Sensitivity analyses controlling for socio-economic status (SES).
Linear mixed effects models associating general cognition with the total cortical representation of fronto-parietal PFNs remain significant across both the discovery and replication samples when controlling for socio-economic status (SES) as measured by areal deprivation index.Pvalues are corrected for multiple comparisons using Bonferroni correction.